A researcher conducted a survey among all the male employees in Research Square Park (just like Research Triangle Park). Among other questions, she asked a randomly selected set of male employees in the past three months (1) how much they have spent on skin-care products and (2) how much tennis they have watched on TV After collecting the data, she performed a simple linear regression analysis. She was interested in the relationship between the time spent on watching tennis and the money spent on skin care. She treated the number of hours in the past three months watching tennis as the independent variable, and she treated the amount spent on skin-care products in the past three months as the dependent variable. She used StatCrunch to get the following output: Simple linear regression results: Dependent Variable: Skincare Independent Variable: Tennis Skincare 56.852733 +3.3255272 Tennis Sample size: 147 R (correlation coefficient) 0.55621421 R-sq 0.3093 7424 Estimate of error standard devilation: 96.483986 Parameter estimates Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 56.852733 9.6919323 Slope 0 145 5.8659854 <0.0001 #0 145 8.0594368 <0.0001 3.3255272 0.41262526 Which of the following are correct interpretations and conclusions of the estimated slope? For simplicity assume all answers are in the context of male employees in RSP over the past three months. Select one or more: a. There is a positive relationship between the time spent on watching tennis and the money spent on skin care. b. There is a negative relationship between the time spent on watching tennis and the money spent on skin care. nc. Each additional hour spent watching tennis is associated with about $3.33 additional money spent on skin care on average. d. Each additional hour spent watching tennis is associated with about $56.85 additional money spent on skin care on average. e. For male RSP employees, watching an additional hour of tennis causes them to spend approximately an additional $3.33 on skin care on average.